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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TimesFM/Fine_tune_TimesFM_on_a_custom_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune TimesFM on a custom dataset\n",
        "\n",
        "In this notebook, we will show that one could fine-tune Google's [TimesFM](https://huggingface.co/docs/transformers/model_doc/timesfm) (Time Series Foundation Model) on a custom dataset of time-series, besides just making zero-shot predictions. This means that we are going to update the weights of the model.\n",
        "\n",
        "## Set-up environment\n",
        "\n",
        "Let's start by installing 🤗 Transformers as well as [PEFT](https://huggingface.co/docs/peft/en/index), which will allow for Parameter-Efficient Fine-Tuning. Make sure to set \"runtime\" to \"GPU\" for faster computation."
      ],
      "metadata": {
        "id": "GJk1OdZ_a_PY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade transformers peft"
      ],
      "metadata": {
        "id": "sM21Ei6Ga_Ib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load dataset\n",
        "\n",
        "Let's load a dataset which hopefully is out-of-distribution for this model, consisting of some Internet-of-Things (IoT) style data which measures requests per second:"
      ],
      "metadata": {
        "id": "-4juXRjvkkOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import zipfile\n",
        "import io\n",
        "import os\n",
        "from glob import glob\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "\n",
        "# URL of the zip file\n",
        "zip_url = \"https://sir-dataset.obs.cn-east-3.myhuaweicloud.com/datasets/public_dataset/public_dataset.zip\"\n",
        "\n",
        "# Directory to extract the files\n",
        "extract_dir = \"public_dataset_extracted\"\n",
        "\n",
        "# Create the extraction directory if it doesn't exist\n",
        "if not os.path.exists(extract_dir):\n",
        "    os.makedirs(extract_dir)\n",
        "\n",
        "# Download the zip file\n",
        "print(f\"Downloading {zip_url}...\")\n",
        "response = requests.get(zip_url)\n",
        "response.raise_for_status() # Raise an exception for bad status codes\n",
        "\n",
        "# Extract the zip file\n",
        "print(\"Extracting the zip file...\")\n",
        "with zipfile.ZipFile(io.BytesIO(response.content)) as z:\n",
        "    z.extractall(extract_dir)\n",
        "print(\"Extraction complete.\")\n",
        "\n",
        "# Path to the requests_minute directory\n",
        "requests_minute_dir = os.path.join(extract_dir, \"public_dataset\", \"csv_files\", \"requests_minute\")\n",
        "filenames_list = glob(requests_minute_dir + '/*.csv')\n",
        "filenames_list.sort()\n",
        "\n",
        "# Reading all columns in all per-minute requests files may take up signficant memory\n",
        "dfs_list = []\n",
        "for filename in tqdm(filenames_list):\n",
        "    # read the entire file, or read just the columns we want by passing usecols = ['time', 'day', 0, 1, 2] to pd.read_csv()\n",
        "    df_i = pd.read_csv(filename, index_col=None)\n",
        "    dfs_list.append(df_i)\n",
        "print('Concatenating dataframes')\n",
        "df_requests = pd.concat(dfs_list)\n",
        "df_requests"
      ],
      "metadata": {
        "id": "SCTlpaXc-U04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "9170fb02-bade-4c6b-8781-6c7acf4f1815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://sir-dataset.obs.cn-east-3.myhuaweicloud.com/datasets/public_dataset/public_dataset.zip...\n",
            "Extracting the zip file...\n",
            "Extraction complete.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26/26 [00:32<00:00,  1.24s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Concatenating dataframes\n"
          ]
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-746cb195-385b-4182-90b4-07e471c47dcb\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-746cb195-385b-4182-90b4-07e471c47dcb')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-746cb195-385b-4182-90b4-07e471c47dcb button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "  <div id=\"id_d370376d-edb3-486f-9540-855caef00529\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_requests')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d370376d-edb3-486f-9540-855caef00529 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_requests');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_requests"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first 2 columns contain the date information (day and time), whereas all other columns contain individual time series, measuring different things.\n",
        "\n",
        "Let's plot the time series of the 3rd column:"
      ],
      "metadata": {
        "id": "Vao58BH_ktuE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(16, 3))\n",
        "\n",
        "plt.scatter(df_requests['time'], df_requests['3'], s=1)\n",
        "plt.xlabel('Time in seconds')\n",
        "plt.ylabel('Requests per minute')\n",
        "plt.title('[Full length] Requests per minute over time for function nr 3')\n",
        "plt.show()\n",
        "\n",
        "n_days = 7\n",
        "fig = plt.figure(figsize=(16, 3))\n",
        "plt.scatter(df_requests['time'], df_requests['3'], s=1)\n",
        "plt.xlabel('Time in seconds')\n",
        "plt.ylabel('Requests per minute')\n",
        "plt.title('[First {} days] Requests per minute over time for function nr 3'.format(n_days))\n",
        "plt.xlim(0, 86400*n_days)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "rzvYyNUKfsE2",
        "outputId": "d0087448-2ed6-4add-88ae-397ff3d2a2e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load model\n",
        "\n",
        "Next we load the pre-trained TimesFM model, released by Google, from the 🤗 hub.\n",
        "\n",
        "We also specify some additional parameters, namely:\n",
        "- load the model in bfloat16 precision (this reduces the memory usage)\n",
        "- use the [\"sdpa\"](https://docs.pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html) version of attention, for faster attention computation\n",
        "- put it on the GPU, if available."
      ],
      "metadata": {
        "id": "tWTDbnZva7Wy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import TimesFmModelForPrediction\n",
        "\n",
        "model = TimesFmModelForPrediction.from_pretrained(\n",
        "    \"google/timesfm-2.0-500m-pytorch\",\n",
        "    attn_implementation=\"sdpa\",\n",
        "    device_map=\"cuda\" if torch.cuda.is_available() else None\n",
        ")"
      ],
      "metadata": {
        "id": "cHY1hl7ta7Po"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform zero-shot forecasting\n",
        "\n",
        "Before starting to fine-tune (updating the weights), we can test out the performance of the model out-of-the-box. Given TimesFM has been pre-trained on millions of time-series, it should already do a decent job.\n",
        "\n",
        "For that we can perform a single forward pass through the model."
      ],
      "metadata": {
        "id": "u_pTXTLAraWn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# take the first time series of the dataset\n",
        "time_series = df_requests['3']"
      ],
      "metadata": {
        "id": "eiVWby5biSR4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use the default context and horizon length of the model\n",
        "context_length = model.config.context_length\n",
        "horizon_length = model.config.horizon_length"
      ],
      "metadata": {
        "id": "6yqUlDcwmBxs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "context_values = time_series[-context_length + horizon_length:]"
      ],
      "metadata": {
        "id": "eEMpATvAmIi_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ground_truth = time_series[-horizon_length:]"
      ],
      "metadata": {
        "id": "OvWpmnEKmDpt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "past_values = torch.tensor(context_values.values).unsqueeze(0).to(device=model.device)"
      ],
      "metadata": {
        "id": "ZpIcWE8-mNV5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also specify the frequency indicator, which in this case is 0, givne the data is per minute (refer to the [README of TimesFM](https://github.com/google-research/timesfm) for more details)."
      ],
      "metadata": {
        "id": "JJABm61ObrDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "frequency_id = [[0]]"
      ],
      "metadata": {
        "id": "hs7LopLDmb4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# forward pass\n",
        "with torch.inference_mode():\n",
        "  outputs = model(past_values=past_values, freq=frequency_id)"
      ],
      "metadata": {
        "id": "UaB2xWxAmi-U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's plot the model's mean predictions alongside the ground truth data:"
      ],
      "metadata": {
        "id": "F9PU7gKQr08b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "context_indices = range(0, context_length-horizon_length)\n",
        "plt.plot(context_indices, context_values, label='Context Data', color='blue')\n",
        "\n",
        "# Plot the ground truth data\n",
        "ground_truth_indices = range(context_length-horizon_length, context_length)\n",
        "plt.plot(ground_truth_indices, ground_truth, label='Ground Truth', color='green')\n",
        "\n",
        "# Plot the predicted values\n",
        "predicted_indices = range(context_length-horizon_length, context_length)\n",
        "plt.plot(predicted_indices, outputs.mean_predictions[0].cpu(), label='Mean Predictions', color='red', linestyle='--')\n",
        "\n",
        "plt.xlabel('Time Index')\n",
        "plt.ylabel('Requests per minute')\n",
        "plt.title(f'TimesFM Forecasting for df_requests[\\'3\\'] (Context Length: {context_length}, Horizon Length: {horizon_length})')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "eYi-dy7Ymsus",
        "outputId": "9f399763-9ca5-40fc-d253-fdc232219eea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create PyTorch dataset\n",
        "\n",
        "Next, let's create a [custom PyTorch dataset](https://docs.pytorch.org/tutorials/beginner/data_loading_tutorial.html?highlight=dataset). For that we need to implement 3 methods:\n",
        "- an init method, which initializes the dataset by setting some variables\n",
        "- a length method, which returns the length of the dataset\n",
        "- a getitem method, which returns one item of the dataset."
      ],
      "metadata": {
        "id": "zKC65RHqr7Qj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import Dataset\n",
        "\n",
        "class TimeSeriesRandomDataset(Dataset):\n",
        "    def __init__(self, time_series, context_length, horizon_length):\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            time_series (numpy.ndarray or pandas.Series): The time series data.\n",
        "            context_length (int): The length of the context window.\n",
        "            horizon_length (int): The length of the horizon window (ground truth).\n",
        "        \"\"\"\n",
        "        if isinstance(time_series, pd.Series):\n",
        "            self.time_series = time_series.dropna().values\n",
        "        else:\n",
        "            self.time_series = time_series\n",
        "            if not isinstance(self.time_series, np.ndarray):\n",
        "                self.time_series = np.array(self.time_series)\n",
        "\n",
        "        self.context_length = context_length\n",
        "        self.horizon_length = horizon_length\n",
        "        self.total_window_length = context_length + horizon_length\n",
        "\n",
        "        # The range of possible starting indices for a window\n",
        "        # A window of total_window_length must fit within the time series\n",
        "        self.max_start_index = len(self.time_series) - self.total_window_length\n",
        "\n",
        "        if self.max_start_index < 0:\n",
        "            raise ValueError(f\"Time series is too short for the specified context ({context_length}) and horizon ({horizon_length}) lengths.\")\n",
        "\n",
        "        # We can define an arbitrary number of \"epochs\" or iterations per actual data point\n",
        "        # For random sampling, the number of samples can be larger than the number of possible distinct windows\n",
        "        self.num_samples = 1000 # You can adjust this number based on your desired dataset size\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.num_samples # Return the predefined number of samples\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        \"\"\"\n",
        "        Returns a random sample containing the context window and the subsequent horizon window.\n",
        "\n",
        "        Args:\n",
        "            idx (int): The index (ignored in random sampling, but required by Dataset).\n",
        "\n",
        "        Returns:\n",
        "            dict: A dictionary containing:\n",
        "                'context': The context window (numpy.ndarray).\n",
        "                'ground_truth': The subsequent horizon window (numpy.ndarray).\n",
        "        \"\"\"\n",
        "        # Generate a random starting index within the valid range\n",
        "        start_index = np.random.randint(0, self.max_start_index + 1)\n",
        "\n",
        "        end_index = start_index + self.total_window_length\n",
        "\n",
        "        window = self.time_series[start_index:end_index]\n",
        "\n",
        "        context = window[:self.context_length]\n",
        "        ground_truth = window[self.context_length:]\n",
        "\n",
        "        return {'context': context, 'ground_truth': ground_truth}"
      ],
      "metadata": {
        "id": "wAIw2b8-o8QO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's instantiate the dataset and create a corresponding PyTorch Dataloader, which allows us to get batches of data.\n",
        "\n",
        "Neural networks are typically trained on batches of data, using a variant of stochastic gradient descent (SGD). We will train with a batch size of 32."
      ],
      "metadata": {
        "id": "f6BEL8n2cHU0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Create the dataset\n",
        "time_series_random_dataset = TimeSeriesRandomDataset(time_series, context_length, horizon_length)\n",
        "\n",
        "# Create the dataloader\n",
        "# batch_size determines how many windows are returned in each batch\n",
        "# `shuffle` is often set to `False` when the dataset itself is random\n",
        "batch_size = 32\n",
        "time_series_random_dataloader = DataLoader(time_series_random_dataset, batch_size=batch_size, shuffle=False) # shuffle=False here as sampling is random"
      ],
      "metadata": {
        "id": "1ZZvj35LtFMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model\n",
        "\n",
        "Next we can train the model in native PyTorch. We apply [LoRa](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora) here, which is an example of a Parameter-Efficient Fine-Tuning method. The idea of LoRa is to only train a couple of linear projection layers on top of the pre-trained base model. This means that we're not going to actually update the pre-trained weights of the TimesFM model: they are kept frozen. The only thing we're going to train are the additional linear layers (also called \"adapters\") on top of the model.\n",
        "\n",
        "Thanks to this, the memory requirements are much lower, since we only need to load the optimizer states for those adapters, and only train those. This allows us to run quite large fine-tuning runs on consumer hardware, like an NVIDIA T4 GPU.\n",
        "\n",
        "Of course, this is different from \"full fine-tuning\" (in which we would update all the weights of the model). If you have the memory for that, then feel free to do so."
      ],
      "metadata": {
        "id": "AfMY2MNXchFv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "from peft import LoraConfig, get_peft_model # Import PEFT components\n",
        "\n",
        "\n",
        "# 1. Define PEFT Configuration (LoRA)\n",
        "# You need to identify the linear layers in TimesFM that you want to apply LoRA to.\n",
        "# This often involves inspecting the model's architecture.\n",
        "# Common targets include attention projection layers (q, k, v, o) and feed-forward layers.\n",
        "# Let's assume for demonstration that 'q_proj', 'k_proj', 'v_proj', 'o_proj' are the target modules.\n",
        "# You might need to adjust this based on the actual layer names in TimesFM.\n",
        "lora_config = LoraConfig(\n",
        "    r=16, # LoRA attention dimension\n",
        "    lora_alpha=16, # Alpha parameter for LoRA scaling\n",
        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"], # Target linear layers\n",
        "    lora_dropout=0.1, # Dropout probability for LoRA layers\n",
        "    bias=\"none\", # Bias type\n",
        ")\n",
        "\n",
        "# 2. Get PEFT Model\n",
        "# Wrap your base model with the PEFT configuration\n",
        "peft_model = get_peft_model(model, lora_config)\n",
        "\n",
        "# Print the number of trainable parameters (should be much smaller than the base model)\n",
        "peft_model.print_trainable_parameters()\n",
        "\n",
        "# 3. Define Optimizer and Scheduler\n",
        "optimizer = torch.optim.AdamW(peft_model.parameters(), lr=5e-5)\n",
        "\n",
        "# You can optionally add a learning rate scheduler\n",
        "# from transformers import get_scheduler\n",
        "# num_epochs = 3\n",
        "# num_training_steps = num_epochs * len(time_series_random_dataloader)\n",
        "# lr_scheduler = get_scheduler(\n",
        "#     name=\"linear\", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps\n",
        "# )\n",
        "\n",
        "# 4. Training Loop\n",
        "num_epochs = 50 # Define the number of training epochs\n",
        "\n",
        "peft_model.train() # Set the model to training mode\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    print(f\"Epoch {epoch + 1}/{num_epochs}\")\n",
        "    progress_bar = tqdm(time_series_random_dataloader, desc=\"Training\")\n",
        "\n",
        "    for batch in progress_bar:\n",
        "        # Move batch data to the device\n",
        "        context_batch = batch['context'].to(device=peft_model.device, dtype=torch.bfloat16 if peft_model.config.torch_dtype == 'bfloat16' else torch.float32)\n",
        "        ground_truth_batch = batch['ground_truth'].to(device=peft_model.device, dtype=torch.bfloat16 if peft_model.config.torch_dtype == 'bfloat16' else torch.float32)\n",
        "\n",
        "        # Determine the frequency ID for the new dataset\n",
        "        # Ensure the frequency ID batch matches the batch size\n",
        "        # Let's assume frequency_id 5 for minutely data as an example (check TimesFM docs)\n",
        "        frequency_id_batch = [[0]] * context_batch.shape[0]\n",
        "\n",
        "        # Forward pass\n",
        "        outputs = peft_model(\n",
        "            past_values=context_batch,\n",
        "            freq=frequency_id_batch,\n",
        "            future_values=ground_truth_batch # Provide ground truth for loss calculation\n",
        "        )\n",
        "\n",
        "        # Get the loss\n",
        "        loss = outputs.loss\n",
        "\n",
        "        # Backward pass and optimize\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        # if lr_scheduler is not None:\n",
        "        #     lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Update progress bar description\n",
        "        progress_bar.set_postfix(loss=loss.item())\n",
        "\n",
        "print(\"\\nTraining finished.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bCDtg-RtUV6",
        "outputId": "6c185238-77a2-49a9-ea66-73c7d96c802c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 8,192,000 || all params: 507,020,960 || trainable%: 1.6157\n",
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:45<00:00,  1.43s/it, loss=3.23e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.48s/it, loss=1.3e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 3/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=3.27e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 4/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.01e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 5/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=3.01e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 6/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=1.32e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 7/50\n"
          ]
        },
        {
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          "text": [
            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=1.29e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 8/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=1.67e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.46s/it, loss=1.29e+5]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10/50\n"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.4e+5]\n"
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        },
        {
          "output_type": "stream",
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          "text": [
            "Epoch 11/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.32e+5]\n"
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        },
        {
          "output_type": "stream",
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          "text": [
            "Epoch 12/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.15e+5]\n"
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        },
        {
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          "text": [
            "Epoch 13/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.36e+5]\n"
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        },
        {
          "output_type": "stream",
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          "text": [
            "Epoch 14/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:46<00:00,  1.47s/it, loss=9.53e+4]\n"
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        },
        {
          "output_type": "stream",
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          "text": [
            "Epoch 15/50\n"
          ]
        },
        {
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        },
        {
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          "text": [
            "Epoch 16/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 17/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 18/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 19/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 20/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 21/50\n"
          ]
        },
        {
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            "Training: 100%|██████████| 32/32 [00:47<00:00,  1.47s/it, loss=1.13e+5]\n"
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        },
        {
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            "Epoch 22/50\n"
          ]
        },
        {
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        },
        {
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            "Epoch 23/50\n"
          ]
        },
        {
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            "Epoch 24/50\n"
          ]
        },
        {
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            "Epoch 25/50\n"
          ]
        },
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            "Epoch 26/50\n"
          ]
        },
        {
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            "Epoch 27/50\n"
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            "Epoch 28/50\n"
          ]
        },
        {
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            "Epoch 29/50\n"
          ]
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        {
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            "Epoch 30/50\n"
          ]
        },
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        {
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            "Epoch 31/50\n"
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        {
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            "Epoch 32/50\n"
          ]
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        {
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            "Epoch 33/50\n"
          ]
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        {
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            "Epoch 34/50\n"
          ]
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        {
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        {
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            "Epoch 35/50\n"
          ]
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        {
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        {
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            "Epoch 36/50\n"
          ]
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        {
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        {
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            "Epoch 37/50\n"
          ]
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        {
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        {
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          "text": [
            "Epoch 38/50\n"
          ]
        },
        {
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        {
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          "text": [
            "Epoch 39/50\n"
          ]
        },
        {
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        {
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            "Epoch 40/50\n"
          ]
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        {
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        {
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            "Epoch 41/50\n"
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          "text": [
            "Epoch 42/50\n"
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        {
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            "Epoch 43/50\n"
          ]
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            "Epoch 44/50\n"
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            "Epoch 45/50\n"
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        {
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            "Epoch 46/50\n"
          ]
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        {
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        {
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          "text": [
            "Epoch 47/50\n"
          ]
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        {
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            "Epoch 48/50\n"
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            "Epoch 49/50\n"
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        {
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            "Epoch 50/50\n"
          ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training finished.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference\n",
        "\n",
        "Once the model is trained, we can perform inference by performing a forward pass."
      ],
      "metadata": {
        "id": "n2z2BG_SccIM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "\n",
        "\n",
        "with torch.inference_mode():\n",
        "  outputs = peft_model(past_values=past_values, freq=frequency_id)\n",
        "\n",
        "context_indices = range(0, context_length-horizon_length)\n",
        "plt.plot(context_indices, context_values, label='Context Data', color='blue')\n",
        "\n",
        "# Plot the ground truth data\n",
        "ground_truth_indices = range(context_length-horizon_length, context_length)\n",
        "plt.plot(ground_truth_indices, ground_truth, label='Ground Truth', color='green')\n",
        "\n",
        "# Plot the predicted values\n",
        "predicted_indices = range(context_length-horizon_length, context_length)\n",
        "plt.plot(predicted_indices, outputs.mean_predictions[0].cpu(), label='Mean Predictions', color='red', linestyle='--')\n",
        "\n",
        "plt.xlabel('Time Index')\n",
        "plt.ylabel('Requests per minute')\n",
        "plt.title(f'TimesFM Forecasting for df_requests[\\'3\\'] (Context Length: {context_length}, Horizon Length: {horizon_length})')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "07FxUxzatqKu",
        "outputId": "338dbe1f-b983-4281-94e9-c6eb2f1082aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}